Similar to [1]}, we represent the architecture \(A\)  of the testee in a differentiable way. The search space of \(A\)  is composed of a large number of building blocks. The output of each block is associated with a variable \(a\)  indicating how important this block is. After learning, blocks whose \(a\)  is among the largest are retained to form the final architecture. In this end, architecture search amounts to optimizing the set of architecture variables \(A=\lbrace a\rbrace \) .
